Galaxy-cluster

Estimating the Dynamical Mass of a Galaxy Cluster

This project analyzes spectroscopic redshift data of galaxies in a given field to estimate the dynamical mass of a galaxy cluster. The analysis includes data cleaning, visualization, velocity calculation, and mass estimation using astrophysical principles and observational data.

📁 Files Used

• Skyserver_SQL6_22_2025 6_29_58 AM.csv: Raw catalog of galaxy data retrieved from SDSS SkyServer.
• code.py: Main Python script that performs the full analysis pipeline.

🔧 Key Tools & Libraries

• pandas for data manipulation
• matplotlib.pyplot for plotting
• numpy for numerical operations
• astropy.constants and astropy.units for physical constants
• astropy.cosmology for cosmological model (Planck18)

🔬 Main Steps

1. Data Preparation

• Read the CSV data.
• Aggregate by objid to remove duplicates and get a single entry per galaxy.

2. Redshift Analysis

• Compute the mean and standard deviation of spectroscopic redshift (specz).
• Apply a 3-sigma filter to isolate galaxies likely belonging to a cluster.

3. Visualizations

• Boxplot and histogram of redshift distribution.
• Histogram of velocity and angular separation.

4. Velocity Calculations

• Convert redshift to expansion velocity using the relativistic formula.
• Calculate velocity dispersion.

5. Spatial Analysis

• Compute average angular separation from the cluster center.

6. Mass Estimation

• Estimate physical diameter from redshift and separation.
• Apply virial theorem to estimate dynamical mass of the cluster.

📊 Results

• Cluster Redshift: Computed as the mean of filtered redshift values.
• Velocity Dispersion: Derived from relativistic redshift velocities.
• Dynamical Mass: Estimated using velocity dispersion and physical diameter.